Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations64104
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.7 MiB
Average record size in memory290.1 B

Variable types

Text1
DateTime1
Numeric7
Categorical3

Alerts

Currency Code has constant value "USD" Constant
Line Total is highly overall correlated with Total Unit Cost and 1 other fieldsHigh correlation
Total Unit Cost is highly overall correlated with Line Total and 1 other fieldsHigh correlation
Unit Price is highly overall correlated with Line Total and 1 other fieldsHigh correlation

Reproduction

Analysis started2025-07-14 02:58:11.172602
Analysis finished2025-07-14 02:58:31.994339
Duration20.82 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct10684
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
2025-07-13T22:58:32.613386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.915855
Min length11

Characters and Unicode

Total characters763854
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSO - 000225
2nd rowSO - 0003378
3rd rowSO - 0005126
4th rowSO - 0005614
5th rowSO - 0005781
ValueCountFrequency (%)
so 64104
33.3%
64104
33.3%
0010430 6
 
< 0.1%
0006279 6
 
< 0.1%
0009903 6
 
< 0.1%
0009346 6
 
< 0.1%
0005126 6
 
< 0.1%
0005614 6
 
< 0.1%
0005781 6
 
< 0.1%
0010394 6
 
< 0.1%
Other values (10676) 64056
33.3%
2025-07-13T22:58:33.562897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 211134
27.6%
128208
16.8%
S 64104
 
8.4%
O 64104
 
8.4%
- 64104
 
8.4%
1 30138
 
3.9%
2 25434
 
3.3%
3 25434
 
3.3%
4 25434
 
3.3%
5 25428
 
3.3%
Other values (4) 100332
13.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 443334
58.0%
Space Separator 128208
 
16.8%
Uppercase Letter 128208
 
16.8%
Dash Punctuation 64104
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 211134
47.6%
1 30138
 
6.8%
2 25434
 
5.7%
3 25434
 
5.7%
4 25434
 
5.7%
5 25428
 
5.7%
6 25428
 
5.7%
7 25338
 
5.7%
8 24798
 
5.6%
9 24768
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
S 64104
50.0%
O 64104
50.0%
Space Separator
ValueCountFrequency (%)
128208
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 635646
83.2%
Latin 128208
 
16.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 211134
33.2%
128208
20.2%
- 64104
 
10.1%
1 30138
 
4.7%
2 25434
 
4.0%
3 25434
 
4.0%
4 25434
 
4.0%
5 25428
 
4.0%
6 25428
 
4.0%
7 25338
 
4.0%
Other values (2) 49566
 
7.8%
Latin
ValueCountFrequency (%)
S 64104
50.0%
O 64104
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 763854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 211134
27.6%
128208
16.8%
S 64104
 
8.4%
O 64104
 
8.4%
- 64104
 
8.4%
1 30138
 
3.9%
2 25434
 
3.3%
3 25434
 
3.3%
4 25434
 
3.3%
5 25428
 
3.3%
Other values (4) 100332
13.1%
Distinct1520
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size500.9 KiB
Minimum2014-01-01 00:00:00
Maximum2018-02-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-13T22:58:33.898844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:34.222820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Customer Name Index
Real number (ℝ)

Distinct175
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.480064
Minimum1
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size500.9 KiB
2025-07-13T22:58:34.515135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q145
median87
Q3130
95-th percentile165
Maximum175
Range174
Interquartile range (IQR)85

Descriptive statistics

Standard deviation49.884946
Coefficient of variation (CV)0.57024359
Kurtosis-1.177726
Mean87.480064
Median Absolute Deviation (MAD)43
Skewness0.0087141618
Sum5607822
Variance2488.5078
MonotonicityNot monotonic
2025-07-13T22:58:34.809163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 540
 
0.8%
80 528
 
0.8%
100 516
 
0.8%
113 516
 
0.8%
109 486
 
0.8%
74 486
 
0.8%
79 480
 
0.7%
41 474
 
0.7%
45 474
 
0.7%
4 468
 
0.7%
Other values (165) 59136
92.3%
ValueCountFrequency (%)
1 342
0.5%
2 366
0.6%
3 330
0.5%
4 468
0.7%
5 384
0.6%
6 342
0.5%
7 462
0.7%
8 222
0.3%
9 354
0.6%
10 324
0.5%
ValueCountFrequency (%)
175 300
0.5%
174 324
0.5%
173 390
0.6%
172 252
0.4%
171 330
0.5%
170 288
0.4%
169 312
0.5%
168 396
0.6%
167 306
0.5%
166 306
0.5%

Channel
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
Wholesale
34596 
Distributor
19968 
Export
9540 

Length

Max length11
Median length9
Mean length9.1765256
Min length6

Characters and Unicode

Total characters588252
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWholesale
2nd rowDistributor
3rd rowWholesale
4th rowExport
5th rowWholesale

Common Values

ValueCountFrequency (%)
Wholesale 34596
54.0%
Distributor 19968
31.1%
Export 9540
 
14.9%

Length

2025-07-13T22:58:35.097386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T22:58:35.399386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wholesale 34596
54.0%
distributor 19968
31.1%
export 9540
 
14.9%

Most occurring characters

ValueCountFrequency (%)
l 69192
11.8%
e 69192
11.8%
o 64104
10.9%
s 54564
9.3%
t 49476
8.4%
r 49476
8.4%
i 39936
6.8%
W 34596
 
5.9%
h 34596
 
5.9%
a 34596
 
5.9%
Other values (6) 88524
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 524148
89.1%
Uppercase Letter 64104
 
10.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 69192
13.2%
e 69192
13.2%
o 64104
12.2%
s 54564
10.4%
t 49476
9.4%
r 49476
9.4%
i 39936
7.6%
h 34596
6.6%
a 34596
6.6%
b 19968
 
3.8%
Other values (3) 39048
7.4%
Uppercase Letter
ValueCountFrequency (%)
W 34596
54.0%
D 19968
31.1%
E 9540
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 588252
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 69192
11.8%
e 69192
11.8%
o 64104
10.9%
s 54564
9.3%
t 49476
8.4%
r 49476
8.4%
i 39936
6.8%
W 34596
 
5.9%
h 34596
 
5.9%
a 34596
 
5.9%
Other values (6) 88524
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 588252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 69192
11.8%
e 69192
11.8%
o 64104
10.9%
s 54564
9.3%
t 49476
8.4%
r 49476
8.4%
i 39936
6.8%
W 34596
 
5.9%
h 34596
 
5.9%
a 34596
 
5.9%
Other values (6) 88524
15.0%

Currency Code
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
USD
64104 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters192312
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 64104
100.0%

Length

2025-07-13T22:58:35.658637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T22:58:35.834823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
usd 64104
100.0%

Most occurring characters

ValueCountFrequency (%)
U 64104
33.3%
S 64104
33.3%
D 64104
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 192312
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 64104
33.3%
S 64104
33.3%
D 64104
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 192312
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 64104
33.3%
S 64104
33.3%
D 64104
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 64104
33.3%
S 64104
33.3%
D 64104
33.3%

Warehouse Code
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
AXW291
30204 
GUT930
14724 
NXH382
12564 
FLR025
6612 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters384624
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAXW291
2nd rowAXW291
3rd rowAXW291
4th rowAXW291
5th rowAXW291

Common Values

ValueCountFrequency (%)
AXW291 30204
47.1%
GUT930 14724
23.0%
NXH382 12564
19.6%
FLR025 6612
 
10.3%

Length

2025-07-13T22:58:36.046244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T22:58:36.268634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
axw291 30204
47.1%
gut930 14724
23.0%
nxh382 12564
19.6%
flr025 6612
 
10.3%

Most occurring characters

ValueCountFrequency (%)
2 49380
12.8%
9 44928
11.7%
X 42768
11.1%
A 30204
 
7.9%
W 30204
 
7.9%
1 30204
 
7.9%
3 27288
 
7.1%
0 21336
 
5.5%
U 14724
 
3.8%
T 14724
 
3.8%
Other values (8) 78864
20.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 192312
50.0%
Uppercase Letter 192312
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 42768
22.2%
A 30204
15.7%
W 30204
15.7%
U 14724
 
7.7%
T 14724
 
7.7%
G 14724
 
7.7%
N 12564
 
6.5%
H 12564
 
6.5%
F 6612
 
3.4%
L 6612
 
3.4%
Decimal Number
ValueCountFrequency (%)
2 49380
25.7%
9 44928
23.4%
1 30204
15.7%
3 27288
14.2%
0 21336
11.1%
8 12564
 
6.5%
5 6612
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 192312
50.0%
Latin 192312
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 42768
22.2%
A 30204
15.7%
W 30204
15.7%
U 14724
 
7.7%
T 14724
 
7.7%
G 14724
 
7.7%
N 12564
 
6.5%
H 12564
 
6.5%
F 6612
 
3.4%
L 6612
 
3.4%
Common
ValueCountFrequency (%)
2 49380
25.7%
9 44928
23.4%
1 30204
15.7%
3 27288
14.2%
0 21336
11.1%
8 12564
 
6.5%
5 6612
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 384624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49380
12.8%
9 44928
11.7%
X 42768
11.1%
A 30204
 
7.9%
W 30204
 
7.9%
1 30204
 
7.9%
3 27288
 
7.1%
0 21336
 
5.5%
U 14724
 
3.8%
T 14724
 
3.8%
Other values (8) 78864
20.5%

Delivery Region Index
Real number (ℝ)

Distinct993
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean495.08661
Minimum1
Maximum994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size500.9 KiB
2025-07-13T22:58:36.641732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52
Q1247
median493
Q3742
95-th percentile944
Maximum994
Range993
Interquartile range (IQR)495

Descriptive statistics

Standard deviation285.64589
Coefficient of variation (CV)0.57696146
Kurtosis-1.1924371
Mean495.08661
Median Absolute Deviation (MAD)247
Skewness0.015030778
Sum31737032
Variance81593.576
MonotonicityNot monotonic
2025-07-13T22:58:36.977995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388 128
 
0.2%
357 121
 
0.2%
125 116
 
0.2%
770 113
 
0.2%
334 112
 
0.2%
533 111
 
0.2%
623 111
 
0.2%
198 111
 
0.2%
980 104
 
0.2%
247 104
 
0.2%
Other values (983) 62973
98.2%
ValueCountFrequency (%)
1 67
0.1%
2 78
0.1%
3 93
0.1%
4 79
0.1%
5 38
0.1%
6 51
0.1%
7 40
0.1%
8 58
0.1%
9 89
0.1%
10 44
0.1%
ValueCountFrequency (%)
994 54
0.1%
993 50
0.1%
992 72
0.1%
991 61
0.1%
990 70
0.1%
989 45
0.1%
988 48
0.1%
987 86
0.1%
986 63
0.1%
985 86
0.1%

Product Description Index
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.913141
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size500.9 KiB
2025-07-13T22:58:37.249282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median15
Q324
95-th percentile27
Maximum30
Range29
Interquartile range (IQR)18

Descriptive statistics

Standard deviation8.7870319
Coefficient of variation (CV)0.58921403
Kurtosis-1.277439
Mean14.913141
Median Absolute Deviation (MAD)9
Skewness-0.0537098
Sum955992
Variance77.21193
MonotonicityNot monotonic
2025-07-13T22:58:37.533595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 5994
 
9.4%
26 5928
 
9.2%
13 3948
 
6.2%
14 3858
 
6.0%
15 3696
 
5.8%
5 3348
 
5.2%
2 3162
 
4.9%
1 2904
 
4.5%
4 2886
 
4.5%
3 2802
 
4.4%
Other values (20) 25578
39.9%
ValueCountFrequency (%)
1 2904
4.5%
2 3162
4.9%
3 2802
4.4%
4 2886
4.5%
5 3348
5.2%
6 2118
3.3%
7 972
 
1.5%
8 1002
 
1.6%
9 810
 
1.3%
10 870
 
1.4%
ValueCountFrequency (%)
30 1026
 
1.6%
29 960
 
1.5%
28 996
 
1.6%
27 1080
 
1.7%
26 5928
9.2%
25 5994
9.4%
24 822
 
1.3%
23 996
 
1.6%
22 942
 
1.5%
21 996
 
1.6%

Order Quantity
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4416885
Minimum5
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size500.9 KiB
2025-07-13T22:58:37.774436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median8
Q310
95-th percentile12
Maximum12
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2762166
Coefficient of variation (CV)0.26963997
Kurtosis-1.2298689
Mean8.4416885
Median Absolute Deviation (MAD)2
Skewness0.025681985
Sum541146
Variance5.1811621
MonotonicityNot monotonic
2025-07-13T22:58:38.008262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6 8298
12.9%
11 8286
12.9%
9 8280
12.9%
5 8160
12.7%
8 8142
12.7%
7 8076
12.6%
10 7560
11.8%
12 7302
11.4%
ValueCountFrequency (%)
5 8160
12.7%
6 8298
12.9%
7 8076
12.6%
8 8142
12.7%
9 8280
12.9%
10 7560
11.8%
11 8286
12.9%
12 7302
11.4%
ValueCountFrequency (%)
12 7302
11.4%
11 8286
12.9%
10 7560
11.8%
9 8280
12.9%
8 8142
12.7%
7 8076
12.6%
6 8298
12.9%
5 8160
12.7%

Unit Price
Real number (ℝ)

High correlation 

Distinct664
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2284.3808
Minimum167.5
Maximum6566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size500.9 KiB
2025-07-13T22:58:38.286398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum167.5
5-th percentile214.4
Q11031.8
median1855.9
Q33606.275
95-th percentile5835.7
Maximum6566
Range6398.5
Interquartile range (IQR)2574.475

Descriptive statistics

Standard deviation1663.5981
Coefficient of variation (CV)0.72824905
Kurtosis-0.19177117
Mean2284.3808
Median Absolute Deviation (MAD)891.1
Skewness0.84365299
Sum1.4643795 × 108
Variance2767558.8
MonotonicityNot monotonic
2025-07-13T22:58:38.559278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051.9 690
 
1.1%
1005 642
 
1.0%
1125.6 630
 
1.0%
1038.5 624
 
1.0%
1078.7 618
 
1.0%
1118.9 600
 
0.9%
1058.6 588
 
0.9%
1112.2 558
 
0.9%
1065.3 546
 
0.9%
1025.1 540
 
0.8%
Other values (654) 58068
90.6%
ValueCountFrequency (%)
167.5 540
0.8%
174.2 396
0.6%
180.9 366
0.6%
187.6 348
0.5%
194.3 396
0.6%
201 408
0.6%
207.7 462
0.7%
214.4 432
0.7%
221.1 360
0.6%
227.8 522
0.8%
ValueCountFrequency (%)
6566 6
 
< 0.1%
6559.3 30
< 0.1%
6552.6 12
 
< 0.1%
6545.9 42
0.1%
6539.2 42
0.1%
6532.5 18
< 0.1%
6525.8 24
< 0.1%
6519.1 6
 
< 0.1%
6512.4 18
< 0.1%
6505.7 36
0.1%

Line Total
Real number (ℝ)

High correlation 

Distinct2535
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19280.683
Minimum837.5
Maximum78711.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size500.9 KiB
2025-07-13T22:58:38.853233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum837.5
5-th percentile1768.8
Q18019.9
median14023.1
Q327416.4
95-th percentile50544.8
Maximum78711.6
Range77874.1
Interquartile range (IQR)19396.5

Descriptive statistics

Standard deviation15429.603
Coefficient of variation (CV)0.80026225
Kurtosis1.1683817
Mean19280.683
Median Absolute Deviation (MAD)8046.7
Skewness1.2250728
Sum1.2359689 × 109
Variance2.3807264 × 108
MonotonicityNot monotonic
2025-07-13T22:58:39.156703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11055 180
 
0.3%
2412 180
 
0.3%
9648 174
 
0.3%
1407 168
 
0.3%
10854 168
 
0.3%
9045 150
 
0.2%
6753.6 150
 
0.2%
1876 138
 
0.2%
8576 138
 
0.2%
10130.4 132
 
0.2%
Other values (2525) 62526
97.5%
ValueCountFrequency (%)
837.5 120
0.2%
871 54
0.1%
904.5 60
0.1%
938 48
 
0.1%
971.5 42
 
0.1%
1005 120
0.2%
1038.5 102
0.2%
1045.2 30
 
< 0.1%
1072 48
 
0.1%
1085.4 84
0.1%
ValueCountFrequency (%)
78711.6 18
< 0.1%
78550.8 6
 
< 0.1%
78470.4 12
< 0.1%
77746.8 6
 
< 0.1%
77505.6 6
 
< 0.1%
77425.2 24
< 0.1%
77344.8 6
 
< 0.1%
77023.2 6
 
< 0.1%
76621.2 6
 
< 0.1%
76540.8 12
< 0.1%

Total Unit Cost
Real number (ℝ)

High correlation 

Distinct5581
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1432.0839
Minimum68.675
Maximum5498.556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size500.9 KiB
2025-07-13T22:58:39.459826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum68.675
5-th percentile130.65
Q1606.216
median1084.4955
Q32046.9338
95-th percentile3672.002
Maximum5498.556
Range5429.881
Interquartile range (IQR)1440.7178

Descriptive statistics

Standard deviation1107.7057
Coefficient of variation (CV)0.7734922
Kurtosis0.65378902
Mean1432.0839
Median Absolute Deviation (MAD)592.146
Skewness1.0873259
Sum91802306
Variance1227012
MonotonicityNot monotonic
2025-07-13T22:58:39.756116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502.5 66
 
0.1%
933.98 66
 
0.1%
743.7 66
 
0.1%
728.96 60
 
0.1%
95.676 60
 
0.1%
726.012 60
 
0.1%
569.5 54
 
0.1%
825.44 54
 
0.1%
563.805 54
 
0.1%
621.76 54
 
0.1%
Other values (5571) 63510
99.1%
ValueCountFrequency (%)
68.675 12
 
< 0.1%
69.68 12
 
< 0.1%
70.35 24
< 0.1%
72.025 18
< 0.1%
72.36 6
 
< 0.1%
73.164 12
 
< 0.1%
73.7 30
< 0.1%
74.169 18
< 0.1%
74.906 18
< 0.1%
75.04 18
< 0.1%
ValueCountFrequency (%)
5498.556 6
< 0.1%
5489.98 12
< 0.1%
5487.3 6
< 0.1%
5444.219 6
< 0.1%
5414.136 6
< 0.1%
5399.731 6
< 0.1%
5371.926 12
< 0.1%
5288.511 6
< 0.1%
5284.96 6
< 0.1%
5219.3 6
< 0.1%

Interactions

2025-07-13T22:58:29.540729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:14.066747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:15.939198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:18.380959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:20.992447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:24.072708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:27.415143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:29.804571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:14.391516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:16.298222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:18.699451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:21.388407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:24.498396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:27.740315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:30.025868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:14.647856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:16.605595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:19.010172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:21.827365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:24.966071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:28.105451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:30.237842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:14.896201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:16.827653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:19.375018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:22.394827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:25.393034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:28.438351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:30.429813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:15.151972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:17.146397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:19.723571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:22.918118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:26.563183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:28.723344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:30.660671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:15.384891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:17.670176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:20.081211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:23.301629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:26.818822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:29.015743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:30.896166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:15.643761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:18.013981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:20.526231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:23.716999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:27.149627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T22:58:29.288156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-07-13T22:58:40.053991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ChannelCustomer Name IndexDelivery Region IndexLine TotalOrder QuantityProduct Description IndexTotal Unit CostUnit PriceWarehouse Code
Channel1.0000.0360.0030.0340.0200.0300.0290.0280.013
Customer Name Index0.0361.000-0.003-0.0140.008-0.035-0.016-0.0190.033
Delivery Region Index0.003-0.0031.0000.0040.001-0.0030.0030.0040.010
Line Total0.034-0.0140.0041.0000.322-0.0100.9010.9340.028
Order Quantity0.0200.0080.0010.3221.000-0.002-0.0070.0000.018
Product Description Index0.030-0.035-0.003-0.010-0.0021.000-0.009-0.0050.035
Total Unit Cost0.029-0.0160.0030.901-0.007-0.0091.0000.9540.029
Unit Price0.028-0.0190.0040.9340.000-0.0050.9541.0000.032
Warehouse Code0.0130.0330.0100.0280.0180.0350.0290.0321.000

Missing values

2025-07-13T22:58:31.184189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-13T22:58:31.667779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OrderNumberOrderDateCustomer Name IndexChannelCurrency CodeWarehouse CodeDelivery Region IndexProduct Description IndexOrder QuantityUnit PriceLine TotalTotal Unit Cost
0SO - 0002252014-01-01126WholesaleUSDAXW2913642762499.114994.61824.343
1SO - 00033782014-01-0196DistributorUSDAXW29148820112351.725868.71269.918
2SO - 00051262014-01-018WholesaleUSDAXW291155266978.25869.2684.740
3SO - 00056142014-01-0142ExportUSDAXW291473772338.316368.11028.852
4SO - 00057812014-01-0173WholesaleUSDAXW291256882291.418331.21260.270
5SO - 00103942014-01-01138ExportUSDNXH38262623115219.357412.34332.019
6SO - 00088652014-01-0153WholesaleUSDGUT930540172874.320120.11667.094
7SO - 00099092014-01-0145WholesaleUSDAXW29115625101045.210452.0679.380
8SO - 00019122014-01-0185WholesaleUSDAXW2919341353852.519262.52966.425
9SO - 00026832014-01-01125ExportUSDAXW29165214122914.534974.01311.525
OrderNumberOrderDateCustomer Name IndexChannelCurrency CodeWarehouse CodeDelivery Region IndexProduct Description IndexOrder QuantityUnit PriceLine TotalTotal Unit Cost
64094SO - 00022002018-02-2875DistributorUSDFLR0254371393819.034371.01565.790
64095SO - 00024632018-02-2834ExportUSDFLR02550412116378.470162.43252.984
64096SO - 00043032018-02-28156WholesaleUSDNXH382268218891.17128.8507.927
64097SO - 00062892018-02-28131WholesaleUSDFLR025301105138.951389.04162.509
64098SO - 00073722018-02-28154DistributorUSDAXW2917845105266.252662.03528.354
64099SO - 00075732018-02-2874WholesaleUSDAXW29182526121815.721788.4980.478
64100SO - 00077062018-02-2851ExportUSDNXH382444216864.35185.8579.081
64101SO - 00077182018-02-28136DistributorUSDAXW29131213113953.043483.02648.510
64102SO - 00080842018-02-28158DistributorUSDAXW2917372073959.727717.92930.178
64103SO - 00086542018-02-2822DistributorUSDAXW291807158998.37986.4848.555